


from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase     		=							logging.get_logger(__name__)


__UpperCamelCase     		=							{
    'nielsr/canine-s': 2_0_4_8,
}

# Unicode defines 1,114,112 total “codepoints”
__UpperCamelCase     		=							1_1_1_4_1_1_2

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
__UpperCamelCase     		=							0

__UpperCamelCase     		=							0XE000
__UpperCamelCase     		=							0XE001
__UpperCamelCase     		=							0XE002
__UpperCamelCase     		=							0XE003
__UpperCamelCase     		=							0XE004

# Maps special codepoints to human-readable names.
__UpperCamelCase     		=							{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
__UpperCamelCase     		=							{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       str 			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




			def __init__(				self   ,    snake_case=chr(snake_case  )   ,    snake_case=chr(snake_case  )   ,    snake_case=chr(snake_case  )   ,    snake_case=chr(snake_case  )   ,    snake_case=chr(snake_case  )   ,    snake_case=chr(snake_case  )   ,    snake_case=False   ,    snake_case=2048   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else bos_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else eos_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else sep_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else cls_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else mask_token

						super().__init__(
						    bos_token=snake_case   ,    eos_token=snake_case   ,    sep_token=snake_case   ,    cls_token=snake_case   ,    pad_token=snake_case   ,    mask_token=snake_case   ,    add_prefix_space=snake_case   ,    model_max_length=snake_case   ,    **snake_case   ,    )

						# Creates a mapping for looking up the IDs of special symbols.
						UpperCamelCase__       =							{}
						for codepoint, name in SPECIAL_CODEPOINTS.items():
									UpperCamelCase__       =							codepoint

						# Creates a mapping for looking up the string forms of special symbol IDs.
						UpperCamelCase__       =							{
						    codepoint: name for name, codepoint in self._special_codepoints.items()
						}

						UpperCamelCase__       =							UNICODE_VOCAB_SIZE
						UpperCamelCase__       =							len(self._special_codepoints  )




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self._unicode_vocab_size




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						return list(snake_case  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						try:
									return ord(snake_case  )
						except TypeError:
									raise ValueError(F'''invalid token: \'{token}\''''  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						try:
									if index in SPECIAL_CODEPOINTS:
												return SPECIAL_CODEPOINTS[index]
									return chr(snake_case  )
						except TypeError:
									raise ValueError(F'''invalid id: {index}'''  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						return "".join(snake_case  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						UpperCamelCase__       =							[self.sep_token_id]
						UpperCamelCase__       =							[self.cls_token_id]

						UpperCamelCase__       =							cls + token_ids_a + sep
						if token_ids_a is not None:
									result += token_ids_a + sep
						return result




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None   ,    snake_case = False  ):

						'''simple docstring'''


						if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=snake_case   ,    token_ids_a=snake_case   ,    already_has_special_tokens=snake_case  )

						UpperCamelCase__       =							[1] + ([0] * len(snake_case  )) + [1]
						if token_ids_a is not None:
									result += ([0] * len(snake_case  )) + [1]
						return result




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						UpperCamelCase__       =							[self.sep_token_id]
						UpperCamelCase__       =							[self.cls_token_id]

						UpperCamelCase__       =							len(cls + token_ids_a + sep  ) * [0]
						if token_ids_a is not None:
									result += len(token_ids_a + sep  ) * [1]
						return result







			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						return ()






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard







@slow
@require_flax
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().tearDown()
						gc.collect()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-canny"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"bird"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([canny_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-openpose"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"Chef in the kitchen"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([pose_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2


